SSD: ING IND-09
From Design to Flow solutions In Industrial Fluid Machinery
Team Research Assistant & PostDoc Giovanni Delibra, Paolo Venturini Ph.D. Candidates David Volponi, Alessio Castorrini Ph.D. students Tommaso Bonanni, Lorenzo Tieghi, Gino Angelini
SSD: ING IND-09
Selection
VIRTUAL TEST RIG
Fluid Machinery R&D Environment
DESIGN Smart environment is configured in order to give the user the possibility to customize the experience selecting tools optimizing time and accuracy.
OPTIMIZATION
TM Design
Fan Selector
Prototyping
Duty Selection Design Verify AxLab
CFD & FSI
ADM ALM
MixLab
P-Track
CentrLab
Test
Der. Design
BD
B A C
D
Configure Tool Chain
Opt
BigData In Fluid
Machinery
SSD: ING IND-09
OPTIMIZATION
DESIGN
VIRTUAL TEST RIG
Integrated tool for industrial design of turbomachinery blades
DESIGN • Develepod in Python, based on an axisymmetric solution of
the indirect problem.
• Designed to be Multi-Platform and Expandable.
• It allows to test innovative design solutions.
• Save, Export, Share and Compare different Designs.
SSD: ING IND-09
OPTIMIZATION
DESIGN
VIRTUAL TEST RIG
VIRTUAL TEST RIG
TM DESIGN
CentrLab
MixLab
AxLab
AADM
ALM
The virtual test rig consists of different analysis tools:
• Reduced Order Solvers: AxLab, MixLab, CentrLab
• Actuator Disk Model
• Actuator Line Model
• Ljungstrom Heat Exchanger
• Each tool is designed for performance analysis on different level; from a
quasi-3D steady state approach to transient problems.
• Set of instruments for fan performance analysis in order to simplify fans
performance prediction.
• Designed both for axial and centrifugal machinery
SSD: ING IND-09
DESIGN
VIRTUAL TEST RIG
Reduced Order Solvers: AxLab – MixLab
TM DESIGN
AxLab and MixLab software are python programs for performance analysis of ducted axial or mixed-flow fans.
• Based on a blade element axisymmetric principle whereby the rotor blade is divided into a number of streamlines.
• For each streamline, relations for velocity and pressure are derived from incompressible conservation laws for mass, tangential momentum and energy.
• Produce dependable results with small computing costs requiring solution of a simplified radial equilibrium equation at only one axial station.
CentrLab
MixLab
AxLab
CentrLab software is a python program centrifugal fans performance analysis.
• Based on the blade-to-blade analysis of a two-dimensional inviscid flow
• Continuity and vorticity equation in cylindrical coordinates are reduced to the blade-to-blade equation by defining a stream function Ψ
• Equation solved on a unstructured grid can be easily automated through well-known algorithms of triangulation
SSD: ING IND-09
• Re-design of High-pressure combustion fan
SSD: ING IND-09
AERODYNAMIC DESIGN OF AGRICULTURAL SPRAYERS UNIT
DIMA Blade Inverse design CFD verification of perfomance & FEA Fieni Industrialization and lab tests
SSD: ING IND-09
DESIGN OF A FAN FOR A SNOW GUN
SSD: ING IND-09
D800 vs D800+
P [kW]
Zrot dBA normalised
weight of rotor blade
D800 18.5 12 84.8 1 D800+ 17.4 6 83.2 0.6 ∆ -1.1 -50% -1.6 dBA -40%
Power requirements of the D800+ blade was 1,1kW lower than D800 Number of blades was halved and an overall 40% of weight reduction was achieved 1.6 dBA reduction of noise was achieved
SSD: ING IND-09
CFD and then? … adventure & experiments me
SSD: ING IND-09
Models for devices in ventilation systems • ADM and ALM for axial flow fans (see other slides)
• Rotating heat exchangers
• collaboration with ENEL
• Gravity dampers and fire shutters • collaboration with GE Oil & GAS
SYNTETIC METHODS CFD & FSI
NUMERICAL RECIPIES
U
SSD: ING IND-09
OPTIMIZATION
DESIGN
VIRTUAL TEST RIG
ACTUATOR DISK MODEL
TM DESIGN AADM
The Actuator Disk Model is implemented in Open Foam (C++ language) and allows a synthetic rotor simulation
• We have developed and validated a Advanced Actuator Disk Model for industrial turbomachinery performance prediction in complex industrial system layouts
• Limitations of AADM: able to reproduce only the average behavior of the fan within the ventilation system, without giving information on the unsteady flow field
SSD: ING IND-09
OPTIMIZATION
DESIGN
VIRTUAL TEST RIG
LJUNGSTROM HEAT EXCHANGER
TM DESIGN A Ljungstrom heat exchanger is used in thermal power plant to preheat the primary air with the exhaust gasses of the combustion process
To reproduce the effect of the Ljungstrom heat exchanger on the fluid, a synthetic model has been implemented in OpenFOAM (C++)
The operating principle of the device is dicretized by:
• A porous mean, modelling the fluid pressure drop
• A source term, added in the temperature equation, modeling the complex heat exchange mechanism
Modelization and coefficients have been derived from experiments and empirical formulations from literature-
SSD: ING IND-09
OPTIMIZATION
DESIGN
VIRTUAL TEST RIG
ACTUATOR LINE MODEL
TM DESIGN ALM
The Actuator Line Model is implemented in Open Foam (C++ language) allows a synthetic rotor simulation
• Created to overcome the limitations of AADM : blades are physically reproduced in the computational geometry assigning force source terms only to some cells
• This kind of approach can be used CFD environment that can be based on any kind of unsteady approach (URANS, LES, hybrid LES/RANS, DNS) providing unsteady system effect on the behavior of the fan
SSD: ING IND-09
Advanced URANS modelling based on recovery of Reynolds stress anisotropy implemented and validated in X++ and OF: • cubic k-ε model • ζ-f model
ADVANCED Multi-Physics MODELLING FOR Industrial FLOWS CFD & FSI
NUMERICAL RECIPIES
U VIRTUAL TEST RIG
Hybrid LES/RANS models implemented and validated in X++ and OF and applied to TM flows • ζ-f model • No-model LES based on DCDD discontinuity capturing operator
(Selected) papers A Corsini, F. Rispoli. Flow analyses in a high-pressure axial ventilation fan with a non-linear eddy-viscosity closure, International Journal of Heat and Fluid Flow G. Delibra, D. Borello, K. Hanjalić, F. Rispoli, URANS of flow and endwall heat transfer in a pinned passage relevant to gas-turbine blade cooling, International Journal of Heat and Fluid Flow
(Selected) papers L. Cardillo, A. Corsini , G. Delibra, F. Rispoli, T. E. Tezduyar, Flow Analysis of a Wave-Energy Air Turbine with the SUPG/PSPG Method and DCDD, Advances in Computational Fluid-Structure Interaction and Flow Simulation, 2016 G. Delibra, D. Borello, K. Hanjalić, F. Rispoli, Vortex structures and heat transfer in a wall-bounded pin matrix: LES with a RANS wall-treatment, International Journal of Heat and Fluid Flow 2010
SSD: ING IND-09
DCDD inner working DIMA_FP Wells Turbine 1.5 kW
LES mesh approx 100 M elements DCDD mesh 3.5 M elements
DCDD mol/ν ν
POSEIDONE consortium
SSD: ING IND-09
PARTICLE TRACKING
CFD & FSI
Direct FSI study on FAN blades: improvement in numerical testing capabilities for virtual prototyping
Castorrini et al. Fluid-structure interaction study of large and light axial fan blade, Proceedings of ASME TurboExpo 2017
Passive morphing control Numerical testing of new concepts for passive flow control
FSI
FLOW CONTROL
Castorrini, A., et al. Numerical Study on the Passive Control of the Aeroelastic Response in Large Axial Fans. Proceedings of ASME TurboExpo 2016 Castorrini et al., Numerical testing of a trailing edge passive morphing control for large axial fan blades, Proceedings of ASME TurboExpo 2017
CAE tool development for FSI applications on rotating blades
SSD: ING IND-09
L=20µm
D=4µm
D=4µm
Simulated particles
sphere (red line) non-sphere (green line) particles
VMS for particle-cloud tracking in turbomachinery flows
Results, particle impact on fan blades – non-spherical
SSD: ING IND-09
Presentation example (magnified)
PARTICLE TRACKING
CFD & FSI Material wearing on turbomachinery blades can produce after years of working, a change in the aerodynamic shape of the profile. The capability to predict the performance degradation during the design phase can be of great help to plan the maintenance interventions
Prediction of erosion/deposit effect on shape and performance of aerodynamic bodies
ONE-WAY COUPLING INTERFACE
1. STEADY RANS 2. PARTICLE CLOUD
MOTION 3. MATERIAL WEARING
4. BOUNDARY DISPLACEMENT
5. MESH MOTION
Castorrini et al., Numerical simulation with adaptive boundary method for predicting time evolution of erosion processes, Proceedings of ASME TurboExpo 2017
Normalized erosion patterns (Blue: no erosion, 0; red: 1) and aerodynamic field (pressure and streamlines) on the first iteration and at the end of the process
As example, taking a metal cylinder immersed in a flow transporting coal ash particles, we can see that after 16 months of operation the eroded shape brings to a change in flow field symmetry and thus to a change of the expected aerodynamic behavior
SSD: ING IND-09
PARTICLE TRACKING
CFD & FSI
Stall control on axial-flow fans: leading edge bumps
Anti-stall ring in tunnel and metro axial flow fans
Axial thrust control in multistage pumps
Projects on Flow Control
FSI
NUMERICAL RECIPIES
U
FLOW CONTROL
SSD: ING IND-09
Stall control is a key issue to be addressed for R&D of compressors and fans. A number of active and passive techniques were developed aiming at increasing the aerodynamic stability of compressors, eg: • variable pitch in-motion Corsini and Rispoli, 2004, Using sweep to extend the stall-free operational range in axial fan rotors, Proceedings of the IMechE, Part A
Bianchi, Corsini, Sheard, 2011, Stall inception, evolution, and control in a low speed axial fan with variable pitch in motion, J. of Eng. for Gas Turbines and Power
We carried-out an investigation of the flow control capabilities of biomimicry-inspired the leading edge bumps on tunnel and metro fans
• Impact on the pressure rise capability and efficiency of the fan • Characterisation of the flow mechanisms associated with the
sinusoidal leading edge
STALL CONTROL ON AXIAL FANS: LEADING EDGE BUMPS CFD & FSI
FLOW CONTROL
BIOMIMICRY
WHALES ARE THE ONLY EXAMPLE OF
EVOLUTION AT HIGH REYNOLDS
(COMPARABLE WITH TM APPLICATIONS)
LEADING EDGE BUMPS
CAN CONTROL FLOW SEPARATION AT THE TRAILING EDGE OF
NACA PROFILES
BUMPS ON FINS OF HUMPBACKS
ALLOW WHALES TO EXECUTE SHARP
MANOUVERS
1
2
3
4
tubercles
APPLICATION TO T&M FANS
5
SSD: ING IND-09
CFD & FSI
FLOW CONTROL
BIOMIMICRY
WHALES ARE THE ONLY EXAMPLE OF
EVOLUTION AT HIGH REYNOLDS
(COMPARABLE WITH TM APPLICATIONS)
LEADING EDGE BUMPS
CAN CONTROL FLOW SEPARATION AT THE TRAILING EDGE OF
NACA PROFILES
BUMPS ON FINS OF HUMPBACKS
ALLOW WHALES TO EXECUTE SHARP
MANOUVERS
1
2
3
4
JFM JWFM
peak
effi
cienc
y pe
ak p
ress
ure
separation tubercles
APPLICATION TO T&M FANS
5
STALL CONTROL ON AXIAL FANS: LEADING EDGE BUMPS
SSD: ING IND-09
A stabilisation ring consists of an annular chamber incorporated in the fan casing, fitting over the fan blades’ leading edge.
It is a casing treatment able to protect a fan driven into stall from stresses that would inevitably result into its mechanical failure
The chamber is supposed to accomodate recirculating flow as the fan is driven into stall, and re-inject it in the main flow upstream of the rotor
This chamber is divided into vanes by cambered fins welded into the annular chamber with a shroud to separate stabilisation ring inlet and discharge
CFD & FSI
FLOW CONTROL
UE GOES GREEN AND ENFORCE
MINIMUM EFFICIENCY GRADES
US FOLLOWS
1
2 IS AN ANTI-STALL
RING AN AVAILABLE CHOICE FOR A FAN
DESIGNER?
AND BTW HOW IT WORKS?
MEASUREMENTS ON
TEST RIG
CFD ANALYSIS
3
The primary characteristic of a fan fitted with a stabilisation ring is continuously rising pressure back to zero flow
The secondary effect of the anti-stall ring is a decrease of efficiency in the stable range of operations
ANTI-STALL RING IN TUNNEL AND METRO AXIAL FANS
SSD: ING IND-09
At PE AND PP the low pressure core on the suction surface of the rotor is not large enough to interact with the ASR chamber
At DS operations it extends further upstream and contracts and expands when interacting with the different fins.
ANTI-STALL RING: CFD
PEAK PRESSURE
θ
AX
∆θ = 0.0 deg ∆θ = 4.5 deg ∆θ = 9.0 deg
DEEP STALL Pressure contours at 99% of the blade span
Pres
sure
fluc
tuat
ions
in
the
ASR
for 4
0° o
f ro
tor r
evol
utio
n (ro
tor p
assin
g th
ree
fins o
f the
ASR
)
CFD & FSI
FLOW CONTROL
UE GOES GREEN AND ENFORCE
MINIMUM EFFICIENCY GRADES
US FOLLOWS
1
2 IS AN ANTI-STALL
RING AN AVAILABLE CHOICE FOR A FAN
DESIGNER?
AND BTW HOW IT WORKS?
MEASUREMENTS ON
TEST RIG
CFD ANALYSIS
3
SSD: ING IND-09
AXIAL THRUST CONTROL IN MULTISTAGE PUMPS CFD & FSI
FLOW CONTROL
REDUCTION OF AXIAL THRUST IN
MULTISTAGE PUMP We have this series of multistage pumps with way better performance of those of our competitors (in terms of both total head and efficiency) Yet more than 20% of our costs come from the system required to balance the axial thrust of the pump. Our competitors don’t have these costs because their axial thrust is negligible Can you help us?
SSD: ING IND-09
CFD ANALYSIS OF PUMP STAGE CFD & FSI
FLOW CONTROL
REDUCTION OF AXIAL THRUST IN
MULTISTAGE PUMP
CFD ANALYSIS OF
DATUM PUMP
ANALISYS OF THRUST ON IMPELLER
1 2 3 4
1 2
4 3
PRESSURE DISTRIBUTION ON IMPELLER SURFACES
ANALISYS OF PRESSURE IN THE STAGE
(MERIDIONAL VIEW)
SSD: ING IND-09
CFD ANALYSIS OF
DATUM PUMP
ANALISYS OF THRUST ON IMPELLER
SOLUTION FROM CROSS-FERTILIZATION
NUMERICAL TEST OF
PROTOTYPE LAB TEST OF PROTOTYPE
FROM CFD ANALYSIS TO FLOW SOLUTIONS
SSD: ING IND-09
CFD & FSI
FLOW CONTROL
REDUCTION OF AXIAL THRUST IN
MULTISTAGE PUMP
Title CFD ANALYSIS OF PUMP STAGE
Participants: Corsini (Ass. Prof.) – Delibra (AdR) – Cardillo (PhD)
Period: 2014 Sponsor: Ebara Pumps Europe
Patent Masashi Obuchi, So Kuroiwa, Dai Sakihama, Renato Groppo, Fabio Balbo, Mariano Matteazzi, Lucio Cardillo, Alessandro Corsini, Giovanni Delibra, Franco Rispoli, , Franco Rispoli “Impeller assembly for centrifugal pumps”. International publication number WO-2016/060221
CFD ANALYSIS OF PUMP STAGE
SSD: ING IND-09
Data Intensive Flow Solutions
OPTIMIZATION
BIG
DATA IN TM
SSD: ING IND-09
OPTIMIZATION TM DESIGN
AxLab, ADM and ALM, consisting in “reduced order” models are also valuable Optimization tools. Reduced Order or “Synthetic Models” are perfect to replace fitness function in optimization process Fitness function is used to summarize, as a single figure of merit, how close a given design solution is to achieving the set aims. Perfect models for : • Genetic Algorithm Optimization;
• Sampling in Metamodeling;
• Gradient descent Optimization.
OPTIMIZATION
Sampling Metamodel
Genetic Algoritm
Gradient Descent
SSD: ING IND-09
OPTIMIZATION: MinWater Fan Design TM DESIGN
Multi-objective optimization
OPTIMIZATION
Sampling Metamodel
More than 400’000 individuals were simulated with AxLab Software in less than a week. Comparing to other works in literature (Carolous and Demeulenaere) time saved for this operation was 98.6%.
MinWaterCSP
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654443
SSD: ING IND-09
Main duties of DIMA team for MinWaterCSP:
• Noise-reduction aimed axial fan design
• Optimization of fan design
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654443
MinWaterCSP OPTIMIZATION: MinWater Fan Design
SSD: ING IND-09
BIG DATA IN TURBOMACHINERY
• Data-intensive techinques, widely named Big Data, allow for new applications of information technologies in science and engineering
• Big Data techniques (PCA, PLS) analysis on large set of data of complex systems are empoyed to provide data correlations, performance indicators or complex cause-effect reltionship models with latent varables
• These thechniques can be applied to industrial process, analysis of energy efficiency or turbomachinery characterization and design
This technology is here applied to turbomachinery; the aim is the study of unknown correlations between design parameters of industrial fans
• Creation of a database platform (MySQL)
• Application of the PCA or PLS analysis (Python) to the dataset analysis of the correlation
• Application of the new correlations to the design and analysis of new geometry (possible interfacing with an optimization process
PCA, PLS analyis
Database interface Correlation analysis
BIG
DATA IN TM
Progetto Ateneo Sapienza 2016
SSD: ING IND-09
APPLICATION TO INDUSTRIAL TM
Q [m3/s]
ΔP [P
a]
Dtip [m]
Hub
ratio
It is possible to make a query on the database considering diferent machine characterization
• On a Ns-Ds plane i.e. considering the distance from the maximum available efficiency (Cordier)
• On a Q-∆p plane i.e. considering a performance characterization
• On a Diameter-HubToShroud ratio plane, i.e. considering a diemension characterization
BIG
DATA IN TM
Progetto Ateneo Sapienza 2016
SSD: ING IND-09
PCA
Input Output
PLS
The database X consist of a large number of uncorrelated variables (N) and is an high-dimensional (K) system
The PCA and PLS are statistical methods which find a linear regression models by orojecting the predicted and observable varaibles to a new space
• The PCA uses an orthogonal transformations to convert a set of observation of possibly correlated variables into a set of values of linearly uncorrelated variables, the principal components
• The PLS uses an iterative algorithm to model the covariance structures in in two spaces. It is a component based approach that allows estimating complex cause-effect relationship models with latent variables
BIG
DATA IN TM
EXPLORATION OF DESIGN SPACE
Progetto Ateneo Sapienza 2016
SSD: ING IND-09
EXPLORATION OF DESIGN SPACE
Different correlations and features can be find in the dataset analysis: • Well-known correlation from literature • The influence of the input parameters on the global performance of all the analyzed
machines (Loading Plots) • The definition of new correlation betwen input and output parameters • The definition of the most important parameters to be considered in an optimization
process
BIG
DATA IN TM
Progetto Ateneo Sapienza 2016
SSD: ING IND-09
Investigation on stall dynamics, variable speed vs variable pitch
NOISE
LAB
FAST PROTOTYPING
Speed control Pitch control
SSD: ING IND-09
DIY probe
DIY probes & FDD in rotating machineries
SSD: ING IND-09
Rapid prototyping and testing
NOISE
LAB
FAST PROTOTYPING Cecilia
3D printing
Centrifugal and axial in-duct test rigs for aerodynamic measurements
Acoustic measurement
SSD: ING IND-09
METABOLISMO DEI SISTEMI ENERGETICI
Sistemi Energetici
Team Research Assistant & PostDoc (5) Paolo Venturini (ASN), Silvia Sangiorgio, Eileen Tortora, Andrea Marchegiani, Sara Feudo Doctoral candidates (2) Fabrizio Bonacina, Francesca Lucchetta Industrial Collaborations Acqua Latina Spa, Elettra Investimenti Spa, ALPLA Spa, SED Soluzioni Energia & Diagnostica Srl
SSD: ING IND-09
DA sviluppati con strumenti analitici avanzati basati su tecniche di machine learning per gestione di big data
Metabolismo dei sistemi energetici – Metodologie: Data analytics
Ener
gy co
nsum
ptio
n
Production
shut-down days
High Efficiency core
VANTAGGIO COMPETITIVO DA • Identificazione di KPI per sistemi ingegneristici complessi (Multivariate Analysis e Graph
Database) • Valutazione del metabolismo del sistema (Pattern Recognition & Clustering),
modellazione delle correlazioni (Graph Database), individuazione delle relazioni nascoste (Graph Mining)
• Ottimizzazione delle strategie di gestione (Algoritmi Genetici)
SSD: ING IND-09
Metabolismo dei sistemi energetici – Metodologie: Social Network Analysis
Correlation Matrix
• Normalized Mutual Information • Transfer Entropy • Etc.
Graph Modeling
Graph Property
Evaluation of system energy performance based on an analysis of sensor data
Weighted Degree Centrality
SSD: ING IND-09
Metabolismo dei sistemi energetici – Metodologie: modellazione multi-agente dei sistemi complessi
MAS architecture allows to represent • System complexity • Relations between elements of the system • Emergent behaviours Within of MAS structure different kinds of analysis methodologies allows to analyse the nonlinear dynamic features of multivariate datasets collected by the monitoring system
Metodologie
SSD: ING IND-09
Metabolismo dei sistemi energetici – Metodologie: modellazione time-dependent
Sviluppo in-house di modelli per la simulazione di tecnologie energetiche specifiche. Sviluppo di logiche di controllo di sistemi energetici complessi, e modellazione. Modellazione e analisi delle prestazioni di sistemi energetici integrati convenzionali/rinnovabili/storage off-grid e grid-connected tramite modellazione transitoria.
• Modularità del sistema energetico • Corretta interpretazione della variabilità delle prestazioni dovute alla aleatorietà delle FER • Valutazione degli effetti di integrazione di tecnologie diverse nello stessa sistema energetico • Valutazione di diverse logiche di gestione dell’energia in sistemi complessi con esigenze
diversificate
Vantaggi
SSD: ING IND-09
DIAGNOSI ENERGETICA DI UNA RETE AD ARIA COMPRESSA
Ener
gy c
onsu
mpt
ion
Production
shut-down days
High Efficiency core
L’area grigia indica i giorni in cui si è verificato un ridotto consumo energetico garantendo al contempo un elevata efficienza del processo (elevati valori di produzione) Ambito
Analisi e valutazione energetica dell’uso energetico aria compressa, ausiliario ad un processo industriale per la produzione di contenitori di plastica Obiettivo Definire un indicatore energetico in grado di tener conto dell’efficienza del processo produttivo Metodologia Analisi statistica multivariata per correlare i dati di monitoraggio del sistema di compressione al processo produttivo
SSD: ING IND-09
Profilo energetico di un forno industriale esercizio fuori progetto
Ambito Analisi e valutazione energetica di un forno industriale impiegato per la cottura del pane Obiettivo Definire delle metriche per la valutazione delle performance energetiche del processo di cottura in grado di tener conto della qualità del prodotto Metodologia Analisi statistica multivariata per correlare i dati di monitoraggio del processo di cottura alla qualità del prodotto i.e. rapporto convezione/irraggiamento
SEC (m3
methane/n.bread)
Section 1 (%)
Section 2 (%)
Section 3 (%)
Test A 1.7 x 10-4 37 29.5 33.5
Test B 1.8 x 10-4 37.7 28.3 34
Le due curve rappresentano due profili energetici al variare delle condizioni di ventilazione del forno (Test A e B) nelle diverse sezioni del forno o fasi del processo di cottura (1, 2 e 3).
SSD: ING IND-09
Modellazione di un sistema industriale mediante Social Network Analysis per reti di sensori
0
0,5
1
1,5
2
2,5
1 16 31 46 61 76 91 106
121
136
151
166
181
196
211
226
241
256
271
286
301
316
331
346
361
376
391
406
421
436
451
466
SSD: ING IND-09
Indicatori di performance energetiche per ammonia chiller industriali
Indicatore di Performance Energetiche multivariato singolo compressore (a), cluster di compressori alta
e bassa pressione (b) ed intero array (c)
a)
c)
b)
ALEA Lazio Spa
SSD: ING IND-09
Modellazione time-dependent. Dissalazione da rinnovabili nelle isole minori
Ambito Modellazione e analisi di un sistema integrato FER/OI per l’approvvigionamento idrico dell’isola di Ponza. Obiettivo Massimizzare l’uso di energia rinnovabile per la dissalazione compatibilmente con i vincoli territoriali e spaziali presenti. Metodologia Valutazione delle tecnologie più opportune. Definizione di una logica di controllo. Modellazione transitoria. Analisi delle prestazioni e confronto con il sistema attuale di approvvigionamento.
SSD: ING IND-09
Modellazione time-dependent. Potenziale da moto ondoso nelle isole minori
POSEIDONE consortium
SSD: ING IND-09
Biofuels
Vegetable oils fuelled common-rail engine ( A. Corsini, V. Giovannoni, S. Nardecchia, F. Rispoli, F. Sciulli, P. Venturini, ECOS2012, 2012, Perugia; Corsini A., Fanfarillo G., Rispoli F., Venturini P., Energy Procedia, 2015; Corsini A., Marchegiani A., Rispoli F., Sciulli F., Venturini P., Energy Procedia, 2015; A. Corsini, R. Di Antonio, G. Di Nucci, A. Marchegiani, F. Rispoli, P. Venturini, Energy Procedia, 2016 )
Fuels used: rapeseed oils, waste cooking oil (WCO), biodiesel, gasoil, gasoil-WCO blends Experimental setup: 1.9 JTD common-rail Diesel engine, dual fuel system, Bosch BEA emissions monitoring unit
Engine installed at DIMA Lab
ICE
LAB
SSD: ING IND-09
PCA based diagnostics in ICE Acceleration
. 20%
. 40%
. 60%
. 80%
. 100%
ICE
LAB
SSD: ING IND-09
One sentence pitch Signals are not to provide numbers but insights
Diapositiva numero 1Diapositiva numero 2Diapositiva numero 3Diapositiva numero 4Diapositiva numero 5Diapositiva numero 6Diapositiva numero 7Diapositiva numero 8Diapositiva numero 9Diapositiva numero 10Diapositiva numero 11Diapositiva numero 12Diapositiva numero 13Diapositiva numero 14Diapositiva numero 15Diapositiva numero 16Diapositiva numero 17Diapositiva numero 19Diapositiva numero 20Diapositiva numero 21Diapositiva numero 22Diapositiva numero 23Diapositiva numero 24Diapositiva numero 25Diapositiva numero 26Diapositiva numero 27Diapositiva numero 28Diapositiva numero 29Diapositiva numero 30Diapositiva numero 31Diapositiva numero 32Diapositiva numero 33Diapositiva numero 34Diapositiva numero 35Diapositiva numero 36Diapositiva numero 37Diapositiva numero 38Diapositiva numero 39Diapositiva numero 40Diapositiva numero 41Diapositiva numero 42Diapositiva numero 43Diapositiva numero 44Diapositiva numero 45Diapositiva numero 46Diapositiva numero 47Diapositiva numero 48Diapositiva numero 49Diapositiva numero 50Diapositiva numero 51Diapositiva numero 52Diapositiva numero 53Diapositiva numero 54
Top Related